Computer vision services
Use computer vision algorithms to recognize people, places, and objects to collect information, analyze it, and build innovative products.
Computer vision aims at recognizing the contents of images as accurately as possible, ideally the way human sight works – but faster thanks to automation. It applies to any type of visual content – photos, graphics, and videos.
Computers can be taught how to detect, recognize, and most importantly identify what they see. It can be used in a variety of industries, such as automotive, healthcare, and manufacturing.
Computer vision applications may serve many purposes. Their primary aim is to facilitate company development thanks to process automation and making better business decisions, specifically based on the analysis of the data collected.
Its aim is to recognize and identify items in the picture. Software can do it based on a library of already classified images, a specification of distinguishable object properties (in classic computer vision algorithms), or by learning itself based on data (in deep learning).
More, it can get better and better in time, on its own. This technology is widely used in driver assistance systems or automated quality control in manufacturing, i.e. looking for faulty items on assembly lines.
This kind of software can work in manifold ways – face detection (finding faces in the picture), face recognition (identifying particular persons in the pictures or videos), and recognizing people’s age, gender, and reading emotions – crucial indicators of customer satisfaction – to analyze it further on.
The apps based on face recognition are often used in healthcare, traffic management, security, or just to automatically confirm if the person buying a beer isn’t a minor.
Computer algorithms categorize, group, and process information for in-depth analysis and relevant insights. Image classification processes images in a way that in the end they’re attributed with a label (a class).
With a high probability, the system knows whether a pic shows a cat, dog, human, etc. The process of labeling is crucial for example in medical image classification to identify the presence of the disease.
Image segmentation is the key step to a deep and complete understanding of what happens in the picture on the pixel level. This solution aims at not only detecting objects but also finding their exact boundaries.
It is widely used in developing self-driving cars, medical purposes, and in everyday use cases such as portrait mode on our cameras, photo editing apps, or virtual dressing rooms in e-commerce.
The OCR technology allows to scan documents, both printed and handwritten – and to convert them into fully editable data available for search and analysis. It allows companies to digitize their resources and to improve customer care by scanning invoices, business cards, and other types of documents – even reproducing their original formatting.
The OCR’s accuracy can be increased by image post-processing to correct any spelling mistakes. It is also able to recognize text appearing in photos and videos for e.g. text analytics, further translation, or to read it to people with vision impairment.
Task and process automation
Computer vision companies will help you automate a variety of processes such as quality control/inspection tasks. You can eliminate faulty products way before they reach your customers thanks to automated detection early in the production process.
Your team will spend considerably less time analyzing reports and data. As a result, you can deliver results and improvements faster.
Accuracy and precision
With deep learning for computer vision, you can improve the precision of your processes. The technology is insensitive to optical illusions, assumptions, or natural fatigue.
It analyzes images pixel by pixel and draws objective conclusions straightaway. This great precision wouldn’t be possible without artificial intelligence.
With computer vision software you can significantly reduce time spent by your team on mundane tasks. Once computers have been taught how to perform their work, it can be done at minimal costs, saving innumerable man-hours.
Machines can work efficiently 24/7 and their work is easily scalable. Thus with computer vision the tasks can be performed more efficiently and quickly, any mistakes detected faster, and evidence-based conclusions more accurate.
Our development process consists of three stages that let our clients minimize the risk and costs of their projects.
We define your challenge, conduct a workshop session and propose an initial solution.
We propose a complete, long-term solution and plan.
We divide your project into smaller pieces that can be achieved within 1-2 sprints and develop the first one.